Overview

Project summary

Goals:

Findings:

Data Overview

Twitter data

Table of data

Here is a sample of the type of twitter information we obtained.

created_at tweet_id full_text user_id user_location geo_type geo_coordinates language retweet_count favorite_count lat lon
Thu Jun 16 01:22:04 +0000 2016 7.432523e+17 #spearmint and #lemon #infused #mojito is our special #coctail tonight at #thehungrycat… https://t.co/66WfhLb3H7 5.432479e+07 Hollywood, California Point c(34.42229571, -119.70542917) en 0 0 34.42230 -119.7054
Thu Nov 08 20:13:29 +0000 2018 1.060626e+18 This fragrance is for those who enjoy the smell of #mint but don’t like the harsh crispness most #mintyfresh #candles emit. 🔥MINTED CUCUMBER is a warm, fresh, clean scent that can be… https://t.co/tjLl8jgDmI 2.299386e+08 Santa Barbara, CA Point c(34.43836295, -119.74743605) en 0 0 34.43836 -119.7474
Mon Jan 12 01:28:40 +0000 2015 5.544499e+17 Day 3 at Golden Bridge SM in the books. 7 kundalini classes to go this month. Nothing like sunset at… http://t.co/GgLabKf2OA 9.520680e+08 Santa Barbara, CA Point c(34.417583, -119.641959) en 0 0 34.41758 -119.6420
Sun Mar 04 04:22:59 +0000 2018 9.701526e+17 All lanes shutdown in both directions until further notice in #Carpinteria on Hwy 192 NB after Cravens Ln and Sycamore Cyn Rd #LAtraffic 6.517110e+07 Southern California Point c(34.4155, -119.5384) en 0 1 34.41550 -119.5384
Tue Oct 16 16:39:09 +0000 2018 1.052238e+18 St. Francis of Assisi is often remembered as the Patron Saint of animals. Earlier this month we celebrated the Feast of St. Francis here on our St. Vincent’s campus. St. Vincent’s held a… https://t.co/pmLOVnqUUV 7.072856e+17 Santa Barbara, CA Point c(34.44264662, -119.76236015) en 0 0 34.44265 -119.7624
Tue Apr 21 21:13:51 +0000 2015 5.906245e+17 I feel like every quidditch TD needs a Tim Gunn in their life. http://t.co/qMtNHwY5w5 1.461168e+09 Costa Mesa, CA Point c(34.41568786, -119.84217672) en 0 2 34.41569 -119.8422
Sat Apr 07 22:13:00 +0000 2018 9.827431e+17 A delicious skinny cucumber mojito at the new Bluewater Grill. Delish! #drinksintheafternoon… https://t.co/mEfxyugEn7 5.554007e+08 Santa Barbara, CA Point c(34.4127726, -119.6887258) en 0 0 34.41277 -119.6887
Fri Mar 06 18:15:12 +0000 2015 5.739097e+17 My stomach hates me 2.031463e+08 San Diego & UCSB Point c(34.41178625, -119.85757962) en 0 0 34.41179 -119.8576
Sat Aug 26 19:59:37 +0000 2017 9.015346e+17 current weather in Santa Barbara: haze, 75°F 83% humidity, wind 7mph, pressure 1014mb 1.203326e+08 Santa Barbara, CA Point c(34.42, -119.7) en 0 0 34.42000 -119.7000
Sun Feb 08 00:44:14 +0000 2015 5.642231e+17 #craftbeer as a means to enjoy this rainy day! http://t.co/onLb0CT711 2.264738e+08 Carpinteria, CA Point c(34.39527977, -119.52174417) en 1 0 34.39528 -119.5217

Caveats

Required crimson hexagon access

Maps

Interactive with cluster markers

As you zoom in on the map, clusters will disaggregate. You can click on blue points to see the tweet.

Tweet density

This is log-transformed. There is a single coordinate that has over 11,000 tweets reported across all years. It is near De La Vina between Islay and Valerio. There is nothing remarkable about this site so I assume it is the default coordinate when people tag “Santa Barbara” generally. The coordinate is 34.4258, -119.714.

Identifying tourists and locals

If the user has self-identified their location as somewhere in the Santa Barbara area, they are designated a local. This includes Carpinteria, Santa Barbara, Montecito, Goleta, Gaviota and UCSB. For the remainder, we use the number of times they have tweeted from Santa Barbara within a year to designate user type. If someone has tweeted across more than 2 months in the same year from Santa Barbara, they are identified as a local. This is consistent with how Eric Fischer determined tourists in his work. This is not fool-proof and there are instances were people visit and tweet from Santa Barbara more than two months a year, especially if they are visiting family or live within a couple hours driving distance.

There are 26408 tweets from tourists and 56468 tweets from locals.

The following map shows tweet log density by locals (top - blue) and tourists (bottom - red).

Identifying nature-based tweets

Applying dictionary

The dictionary is “nature-based” and is a list of words I put together. I had a hard time finding an ontology or lexicon that would fit this project. These are definitely skewed more towards nature and recreation rather than words like “home” or “connection”.

##  [1] "hike"        "trail"       "hiking"      "camping"     "tent"       
##  [6] "climb"       "summit"      "fishing"     "sail"        "sailing"    
## [11] "boat"        "boating"     "ship"        "cruise"      "cruising"   
## [16] "bike"        "biking"      "dive"        "diving"      "surf"       
## [21] "surfing"     "paddle"      "swim"        "ocean"       "beach"      
## [26] "^sea"        "sand"        "coast"       "island"      "wave"       
## [31] "fish"        "whale"       "dolphin"     "pacific"     "crab"       
## [36] "lobster"     "water"       "shore"       "marine"      "seawater"   
## [41] "lagoon"      "slough"      "saltwater"   "underwater"  "tide"       
## [46] "aquatic"     "^tree"       "^earth"      "weather"     "sunset"     
## [51] "sunrise"     "^sun"        "climate"     "park"        "wildlife"   
## [56] "^view"       "habitat"     "^rock"       "nature"      "mountains"  
## [61] "^peak"       "canyon"      "pier"        "wharf"       "environment"
## [66] "ecosystem"

Where are nature-based tweets?

Are tweets in protected areas more often nature-based?

California Protected Areas Database

Time

Timeline of tweets

Initial hypothesis was identifying spikes in nature-based tweets around three significant events: - Refugio oil spill in 2015 - Thomas fire in 2017 - Debris flow in 2018

Word clouds

top 100 words for locals vs tourist. And we could do this in space. At sterns wharf what are people tweeting about? At Elings, what are locals tweeting about?

Maybe in word clouds we can see some changes due to natural events

All of SB

By area

Sentiment Analysis

Lessons learned

Data is harder to find

Future research

Looking at different scale areas

There might be an interesting comparison between rural-suburban-urban areas. We hypothseize that the tourist/local alignment would split in urban areas, maybe aligned in suburban (like SB) and maybe not exist in rural.

Proportion of words that are nature based tells you how people. In Santa Barbara, there will be a lot of nature-based sense of place. In Manhattan, we wouldn’t expect to see nature based ones so much.

In a blog piece we can pose questions that we couldn’t answer but stuff like “can proportion of tourists/locals in place engagement tell us anything”.

Could compare % nature based tweets in SB to other areas. If we did this across the whole state, what proportion% are nature based? Maybe on average its just 5%.

Where and why do locals and tourists overlap in their use of area. SB seems to have a high alignment of tourists/locals, which may be helpful for local policy. Maybe places with distinct differences in how tourists/locals use places.

Look at cities of different coastal sizes rural - small town - urban - mega city. Could see how tourists/locals patterns differentiate across scale.

Is there a threshold of tourists where locals don’t go anymore?

In areas where we see both tourists and locals engaging, what characteristics do we see?

Quantifying transitions between rural to city.